Data processing may be performed using complex data analytics techniques. For example, a large amount of input data may be processed by a pipeline of artificial intelligence (AI) models including but not limited to convolutional neural networks to generate outputs that contain hidden correlations and features in the input data. A platform for performing such data analytics may be hosted in backend computer servers and detached from a front-end user interface running on a remote user terminal device, and the corresponding AI models may be instantiated in the backend computer servers to form a data analytics pipeline when such data analytics service is requested from the remote terminal. Upon receiving a request for data analytics, the backend servers may need a significant amount of time to configure the data analytics pipeline, to load the AI models and target data, and to calculate intermediate data items and final output. In many applications, a real-time or near-real time response to a request for detached data analytics service may be desired and extended data processing delays may be unacceptable.